1. Field of the Invention
The present invention relates to an obstacle detection apparatus, particularly to an obstacle detection apparatus to detect an obstacle on a road such as a preceding vehicle surround a vehicle, a parking vehicle or a pedestrian.
2. Description of the Related Art
It is important for realizing car safety support system or automatic driving system to detect obstacles in the surrounding environment. There are two methods to detect obstacles. One is to use active sensors such as laser radar or millimeter wave radar. The other is to use passive sensors such as video cameras.
A system which uses video cameras is capable of detecting not only obstacles but also lane markings on the road. This is an advantage of a system which uses video cameras. There is a big advantage capable of realizing a comparatively low cost system by use of a general-purpose device such as a camera.
It is possible to detect obstacles with a single camera system by using techniques such as motion stereo or pattern recognition. However, the system requires a bulky calculation amount to make it difficult to perform a real-time processing, and must be improved in precision, too. For the reasons, it is practical to obtain stereovision by using two or more cameras. The stereo vision is based on the principle of triangulation.
Suppose there are two cameras, left and right, and the relative position between the cameras are known. The three-dimensional position of an object can be obtained if the correspondence of the projected images of the object on the left and the right cameras is provided.
Accordingly, in stereovision, a work called with calibration for obtaining parameters concerning a positional relation between the camera and a work called with corresponding point search for obtaining correspondence relation between images are necessary.
It is necessary for performing a calibration of conventional stereo cameras to capture a number of points known and dispersed in three-dimension position and provide a corresponding relation of the projection points of the captured points between the cameras. This needs a large amount of labor.
It is necessary for knowing a distance from each of the points to an object on an image to do corresponding point search for all points. However, it is not always possible to find correct correspondence for all the points in a pair of images. Wrong distance information is provided when a set of wrong corresponding points are given. This is very undesirable in view of support of safe driving of a car.
On the other hand, if we only need to distinguish obstacle areas from a road area, there is a method which does not need correspondence search nor complicated calibration.
Suppose a point on a road surface is projected to a point (u, v) on the left image and point (u′, v′) on the right image, the relation between (u, v) and (u′, v′) is expressed by the following equation:
                                          u            ′                    =                                                                      h                  11                                ⁢                u                            +                                                h                  12                                ⁢                v                            +                              h                13                                                                                      h                  31                                ⁢                u                            +                                                h                  32                                ⁢                v                            +                              h                33                                                    ,                              v            ′                    =                                                                      h                  21                                ⁢                u                            +                                                h                  22                                ⁢                v                            +                              h                23                                                                                      h                  31                                ⁢                u                            +                                                h                  32                                ⁢                v                            +                              h                33                                                                        (        1        )            
This is referred to as a road planarity constraint equation hereinafter. h={h11, h12, h13, h21, h22, h23, h31, h32, h33} represents a parameter dependent upon a position and posture of each camera to the road surface, a focal distance of optical lens of each camera, and an image origin. This is previously obtained from a set of right and left projection points of points not less than four points on the road surface (ui, vi) (u′i, v′i) {i=1, 2, . . . , N}. If an arbitrary point P (u, v) on one image is assumed to exist on the road surface, a corresponding point P′ (u′, v′) on the other image is obtained by this relational equation. If the point P exists on the road surface, the points P and P′ are a set of correct corresponding points. Therefore, a small partial image around the point P matches a small partial image around the point P′.
On the contrary, if the point P does not exist on the road surface, the points P and P′ are not a set of correct corresponding points. In this case, the partial images do not match well.
Accordingly, it can be determined whether or not an arbitrary point on the image exists on the road surface by comparing the partial images based on the correspondence provided by the equation (1).
This technique is called a plane projection stereo method and disclosed in Japanese Patent Laid-Open No. 2001-76128, for example. The plane projection stereo method has an advantage that calibration is easy and corresponding point search is not necessary. Though this technique can separate a road area and an obstacle area, it has a problem that it cannot grasp the precise position and distance.
As above described, when an obstacle is detected by conventional stereovision, a complicated calibration work needs. There is a problem on reliability of measurement so that the failure of corresponding point searching results in providing error distance information. On the other hand, where an obstacle is detected by a plane projection stereo method, there is a problem on precision of measurement in terms of position and distance.